ASTM D6233-98(2009)
(Guide)Standard Guide for Data Assessment for Environmental Waste Management Activities (Withdrawn 2016)
Standard Guide for Data Assessment for Environmental Waste Management Activities (Withdrawn 2016)
SIGNIFICANCE AND USE
This guide presents a logical process for determining the usability of environmental data for decision making activities. The process describes a series of steps to determine if the enviromental data were collected as planned by the project team and to determine if the a priori expectations/assumptions of the team were met.
This guide identifies the technical issues pertinent to the integrity of the environmental sample collection and analysis process. It guides the data assessor and data user about the appropriate action to take when data fail to meet acceptable standards of quality and reliability.
The guide discusses, in practical terms, the proper application of statistical procedures to evaluate the database. It emphasizes the major issues to be considered and provides references to more thorough statistical treatments for those users involved in detailed statistical assessments.
This guide is intended for those who are responsible for making decisions about environmental waste management projects.
SCOPE
1.1 This guide covers a practical strategy for examining an environmental project data collection effort and the resulting data to determine if they will support the intended use. It covers the review of project activities to determine conformance with the project plan and impact on data usability. This guide also leads the user through a logical sequence to determine which statistical protocols should be applied to the data.
1.1.1 This guide does not establish criteria for the acceptance or use of data but instructs the assessor/user to use the criteria established by the project team during the planning (data quality objective process), and optimization and implementation (sampling and analysis plan) process.
1.2 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this standard.
1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.
WITHDRAWN RATIONALE
This guide covers a practical strategy for examining an environmental project data collection effort and the resulting data to determine if they will support the intended use. It covers the review of project activities to determine conformance with the project plan and impact on data usability. This guide also leads the user through a logical sequence to determine which statistical protocols should be applied to the data.
Formerly under the jurisdiction of Committee D34 on Waste Management, this guide was withdrawn in May 2016. This standard is being withdrawn without replacement because the scope of the standard is much broader than indicated by its title.
General Information
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Standards Content (Sample)
NOTICE: This standard has either been superseded and replaced by a new version or withdrawn.
Contact ASTM International (www.astm.org) for the latest information
Designation: D6233 − 98(Reapproved 2009)
Standard Guide for
Data Assessment for Environmental Waste Management
Activities
This standard is issued under the fixed designation D6233; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope lated to Waste Management Activities: Development of
Data Quality Objectives
1.1 This guide covers a practical strategy for examining an
environmental project data collection effort and the resulting
3. Terminology
data to determine if they will support the intended use. It
covers the review of project activities to determine confor-
3.1 Definitions of Terms Specific to This Standard:
mance with the project plan and impact on data usability. This
3.1.1 bias, n—a systematic error that is consistently nega-
guide also leads the user through a logical sequence to
tive or consistently positive.
determine which statistical protocols should be applied to the
3.1.2 characteristic, n—a property of items in a sample or
data.
population which can be measured, counted, or otherwise
1.1.1 This guide does not establish criteria for the accep-
observed.
tance or use of data but instructs the assessor/user to use the
criteria established by the project team during the planning
3.1.3 composite sample, n—a physical combination of two
(data quality objective process), and optimization and imple-
or more samples.
mentation (sampling and analysis plan) process.
3.1.4 confidence limit, n—an upper and/or lower limit(s)
1.2 The values stated in SI units are to be regarded as
within which the true value is likely to be contained with a
standard. No other units of measurement are included in this
stated probability or confidence.
standard.
3.1.5 continuous data, n—data where the values of the
1.3 This standard does not purport to address all of the
individual samples may vary from minus infinity to plus
safety concerns, if any, associated with its use. It is the
infinity.
responsibility of the user of this standard to establish appro-
priate safety and health practices and determine the applica-
3.1.6 data quality objectives (DQOs), n—DQOs are quali-
bility of regulatory limitations prior to use.
tative and quantitative statements derived from the DQO
process describing the decision rules and the uncertainties of
2. Referenced Documents
the decision(s) within the context of the problem(s).
2.1 ASTM Standards:
3.1.7 data quality objective process, n—a quality manage-
D4687 Guide for General Planning of Waste Sampling
ment tool based on the scientific method and developed to
D5088 Practice for Decontamination of Field Equipment
facilitate the planning of environmental data collection activi-
Used at Waste Sites
ties.
D5283 Practice for Generation of Environmental Data Re-
lated to Waste ManagementActivities: QualityAssurance
3.1.8 discrete data, n—data made up of sample results that
and Quality Control Planning and Implementation
are expressed as a simple pass/fail, yes/no, or positive/
D5792 Practice for Generation of Environmental Data Re-
negative.
3.1.9 heterogeneity, n—the condition of the population un-
This guide is under the jurisdiction of ASTM Committee D34 on Waste
der which all items of the population are not identical with
Management and is the direct responsibility of SubcommitteeD34.01.01 on Plan-
respect to the parameter of interest.
ning for Sampling.
Current edition approvedFeb. 1, 2009. Published March 2009. Originally
3.1.10 homogeneity, n—the condition of the population
approvedin1998.Lastpreviouseditionapprovedin2003asD6233-98(2003).DOI:
under which all items of the population are identical with
10.1520/D6233-98R09.
respect to the parameter of interest.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
3.1.11 population, n—the totality of items or units under
Standards volume information, refer to the standard’s Document Summary page on
the ASTM website. consideration.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
D6233 − 98 (2009)
TABLE 1 Information Needed to Evaluate the Integrity of the
3.1.12 representative sample, n—a sample collected in such
Environmental Sample Collection and Analysis Process
a manner that it reflects one or more characteristics of interest
General Project Details Site History
(as defined by the project objectives) of a population from
Process Description
which it is collected.
Waste Generation Records
Waste Handling/Disposal Practices
3.1.13 sample, n—a portion of material which is taken from
Sources of Contamination
a larger quantity for the purpose of estimating properties or
Conceptual Site Model
composition of the larger quantity. Potential Contaminants of Concern
Fate and Transport Mechanisms
3.1.14 sampling design error, n—error which results from
Exposure Pathways
Boundaries of the Study Area
the unavoidable limitations faced when media with inherently
Adjacent Properties
variable qualities are measured and incorrect judgement on the
part of the project team.
Sampling Issues Sampling Strategy
Sample Location
3.1.15 subsample, n—a portion of a sample that is taken for
Sample Number
testing or for record purposes. Sample Matrix
Sample Volume/Mass
Discrete/Composite Samples
4. Significance and Use
Sample Representativeness
Sampling Equipment, Containers and
4.1 Thisguidepresentsalogicalprocessfordeterminingthe
Preservatives
usability of environmental data for decision making activities.
Analytical Issues Laboratory Sub-sampling
The process describes a series of steps to determine if the
Sample Preparation Methods
enviromental data were collected as planned by the project
Analytical Method
Detection Limits
team and to determine if the a priori expectations/assumptions
Matrix Interferences
of the team were met.
Bias
Holding Times
4.2 This guide identifies the technical issues pertinent to the
Calibration
integrity of the environmental sample collection and analysis
Quality Control Results
process. It guides the data assessor and data user about the Contamination
Reporting Requirements
appropriate action to take when data fail to meet acceptable
Reagents/Supplies
standards of quality and reliability.
Validation and
Data Quality Objectives
4.3 The guide discusses, in practical terms, the proper
Assessment
application of statistical procedures to evaluate the database. It Chain of Custody
Action Level
emphasizes the major issues to be considered and provides
Completeness
references to more thorough statistical treatments for those
Laboratory Audit Results
Field and Laboratory Records
users involved in detailed statistical assessments.
Level of Uncertainty in Reported Values
4.4 This guide is intended for those who are responsible for
making decisions about environmental waste management
projects.
5.3 Appropriate professionals must assess the project plan-
ning documents and completed project records to determine if
5. General Considerations
the project findings match the conceptual model and decision
5.1 This guide provides general guidance about applying
logic. In areas where the findings don’t match, the assessors
numerical and other techniques to the assessment of data
must document and report their findings and, if possible, the
resulting form environmental data collection activities associ-
potential impact on the decision process. Items subject to
ated with waste management activities.
numerical confirmation are compared to the project plan and
any discrepancies and their potential impact noted.
5.2 The environmental measurement process is a complex
process requiring input from a variety of personnel to properly 5.4 Effective quality control (QC) programs are those that
address the numerous issues related to the integrity of the empower the individuals performing the work to evaluate their
sample collection and measurement process in sufficient detail. performance and implement real-time corrections during the
Table 1 lists many of the topics that are common to most sampling or measurement process, or both. When quality
environmental projects. A well-executed project planning ac- control processes (including documentation) are properly
tivity (see Guide D4687, Practices D5088, D5283, and D5792) implemented, they result in data sets (see Fig. 1) that are
should consider the impact of each of these issues on the generated by in-control processes or out-of control processes
reliability of the final project decision. The data assessment that were not amenable to corrective action but whose details
process must then evaluate the actual performance in these are explained by the project staff conducting the work. Good
areas versus that expected by the project planners. Significant QC programs lead to reliable data that are seldom called into
deviations from the a priori performance level of any one or question during the assessment process. However, in cases
combination of these issues may impact the reliability of the where the absence of staff responsibility or authority to
project decision and necessitate a reconsideration of the self-monitor and correct deficiencies at the working level is
decision criteria by the project decision makers. missing, the burden of assuring data integrity is placed on the
D6233 − 98 (2009)
FIG. 1 General Strategy for Assessment of Continuous Data Sets
quality assurance (QA) function. The data assessment process able errors using the quality assurance process. These unmea-
must determine the location (working level or QAlevel) where surable sources of error are often the greatest source of
effective quality control occurs (detection of error and execu- uncertainty in the data collected for environmental projects.
tion of corrective action) in the data collection process and Examples of unmeasurable factors are given in Table 2.
focus on how well the QC function was executed.As a general
5.6 Once the data assessment process has determined the
rule, if the QC function is not executed in real-time and
degree to which the actual data collection effort met the
thoroughly documented by the staff performing the work, the
expectations of the planners, the assessment process moves
more likely the data assessor will be to find questionable data.
into the next phase to determine if the data generated by the
5.5 In addition to addressing the issues listed in Table 1, the effort can be verified and validated and whether it pass
data assessment process must search for unmeasurable factors statistical tests for useability. These issues are discussed in the
whose impact cannot be detected by the review of the project next sections.
records against expectations or numerical techniques. These
6. Sources of Sampling Error
are the types of things that are controlled by effective quality
assurance programs, standard operating procedures, documen-
6.1 Sample collection may cause random or systematic
tation practices, and staff training. Historically, efforts have
errors. Random error affects the data by increasing the
been focused on the control of data collection errors through
imprecision, whereas systemic error biases the data. The data
data review and the quality control process but little emphasis
assessment process should examine the available sampling
has been placed on the detection and evaluation of immeasur-
records to determine if errors were introduced by improper
sampling. A discussion of some of the more common sources
TABLE 2 Examples of Unmeasurable Factors Affecting the of error follow.
Integrity of Environmental Data Collection Efforts
6.1.1 Random Error:
Biased Sampling/Subsampling Incorrect Dilutions 6.1.1.1 Flawsinthesamplingdesignwhichresultintoofew
Sampling Wrong Area or Material Incorrect Documentation
quality control samples being taken in the field can result in
Sample Switching (Mis-labeling) Matrix-Specific Artifacts
undetected errors in the sampling program.Adequate numbers
Misweighing/Misaliquoting
of field QC samples (for example, field splits, co-located
D6233 − 98 (2009)
samples, equipment rinsate blanks, and trip blanks) are neces- water sample containing suspended solids might dissolve
sary to assess inconsistencies in sample collection practices, metals from the solids, resulting in an incorrect high concen-
contaminated equipment, and contamination during the ship- tration being reported. Failure to preserve water samples
ment process. intended for organic analysis may allow significant biological
alteration of the sample.
6.1.1.2 Variations (heterogeneity) in the media being
sampled can cause concentration and property differences 6.2.6 The time of day and prevailing weather conditions
whensamplesarecollectedcanaffectthesample.Forexample,
between and within samples. Field sampling and laboratory
sub-sampling records should be examined to determine if strong winds can blow dust that can contaminate the samples.
Cool mornings or evening can lead to higher retention of
heterogeneity was noted. This can explain wide variations in
field and/or laboratory duplicate data. volatile components in near-surface soil samples compared to
the samples collected in the heat of the day.
6.1.1.3 Samples from the same population (including co-
6.2.7 The above examples only serve to illustrate the need
located samples) can be very different from each other. For
for an experienced professional to review the sampling activi-
example, one sample might be taken from a hot spot that was
ties and to place the resulting analytical data in the proper
not visually obvious while the other was taken outside the
context of the sampling activity. Such assessments add mate-
perimeter of the hot spot. If data from areas of high concen-
rially to the usability of the data.
tration is contained in data sets consisting primarily of uncon-
taminatedmaterial,statisticaloutlieranalysismightsuggestthe
7. Sources of Analytical Error
sample data should be omitted from consideration when
evaluating a site. This can cause serious decision errors. Prior
7.1 Variation in the analytical process may cause random or
to declaring the data point(s) to be outliers, it is important for systematic error. Random error affects the data by increasing
the assessor to examine the QC records from the analysis
the imprecision, whereas systematic error i
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